MapReduce for Large-Scale Monitor Data Analyses

2014 ◽  
Author(s):  
Jianwei Ding ◽  
Yingbo Liu ◽  
Li Zhang ◽  
Jianmin Wang
Keyword(s):  
Large Scale ◽  
Data Analyses ◽  
Monitor Data

scholarly journals Using Galaxy to Perform Large‐Scale Interactive Data Analyses—An Update

2021 ◽  
Vol 1 (2) ◽  
Author(s):  
Alexander Ostrovsky ◽  
Jennifer Hillman‐Jackson ◽  
Dave Bouvier ◽  
Dave Clements ◽  
Enis Afgan ◽  
...  
Keyword(s):  
Large Scale ◽  
Data Analyses ◽  
Interactive Data

scholarly journals Opportunities for increased reproducibility and replicability of developmental neuroimaging

2019 ◽  
Author(s):  
Eduard Klapwijk ◽  
Wouter van den Bos ◽  
Christian K. Tamnes ◽  
Nora Maria Raschle ◽  
Kathryn L. Mills
Keyword(s):  
Data Collection ◽  
Cognitive Neuroscience ◽  
Statistical Power ◽  
Large Scale ◽  
Small Samples ◽  
Developmental Studies ◽  
Data Analyses ◽  
Methodological Rigor ◽  
Research Findings ◽  
Tools And Techniques

Many workflows and tools that aim to increase the reproducibility and replicability of research findings have been suggested. In this review, we discuss the opportunities that these efforts offer for the field of developmental cognitive neuroscience, in particular developmental neuroimaging. We focus on issues broadly related to statistical power and to flexibility and transparency in data analyses. Critical considerations relating to statistical power include challenges in recruitment and testing of young populations, how to increase the value of studies with small samples, and the opportunities and challenges related to working with large-scale datasets. Developmental studies involve challenges such as choices about age groupings, lifespan modelling, analyses of longitudinal changes, and data that can be processed and analyzed in a multitude of ways. Flexibility in data acquisition, analyses and description may thereby greatly impact results. We discuss methods for improving transparency in developmental neuroimaging, and how preregistration can improve methodological rigor. While outlining challenges and issues that may arise before, during, and after data collection, solutions and resources are highlighted aiding to overcome some of these. Since the number of useful tools and techniques is ever-growing, we highlight the fact that many practices can be implemented stepwise.

scholarly journals Tropical Cyclogenesis Associated with Rossby Wave Energy Dispersion of a Preexisting Typhoon. Part I: Satellite Data Analyses*

2006 ◽  
Vol 63 (5) ◽  
pp. 1377-1389 ◽  
Author(s):  
Tim Li ◽  
Bing Fu
Keyword(s):  
Rossby Wave ◽  
Satellite Data ◽  
Wave Energy ◽  
Large Scale ◽  
Wave Train ◽  
Vertical Shear ◽  
Energy Dispersion ◽  
Data Analyses ◽  
Wave Trains ◽  
Rossby Wave Train

Abstract The structure and evolution characteristics of Rossby wave trains induced by tropical cyclone (TC) energy dispersion are revealed based on the Quick Scatterometer (QuikSCAT) and Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) data. Among 34 cyclogenesis cases analyzed in the western North Pacific during 2000–01 typhoon seasons, six cases are associated with the Rossby wave energy dispersion of a preexisting TC. The wave trains are oriented in a northwest–southeast direction, with alternating cyclonic and anticyclonic vorticity circulation. A typical wavelength of the wave train is about 2500 km. The TC genesis is observed in the cyclonic circulation region of the wave train, possibly through a scale contraction process. The satellite data analyses reveal that not all TCs have a Rossby wave train in their wakes. The occurrence of the Rossby wave train depends to a certain extent on the TC intensity and the background flow. Whether or not a Rossby wave train can finally lead to cyclogenesis depends on large-scale dynamic and thermodynamic conditions related to both the change of the seasonal mean state and the phase of the tropical intraseasonal oscillation. Stronger low-level convergence and cyclonic vorticity, weaker vertical shear, and greater midtropospheric moisture are among the favorable large-scale conditions. The rebuilding process of a conditional unstable stratification is important in regulating the frequency of TC genesis.

scholarly journals MicroED data collection with SerialEM

2018 ◽  
Author(s):  
M. Jason de la Cruz ◽  
Michael W. Martynowycz ◽  
Johan Hattne ◽  
Tamir Gonen
Keyword(s):  
Data Collection ◽  
High Throughput ◽  
Large Scale ◽  
Large Scale Data ◽  
Data Analyses ◽  
Continuous Rotation ◽  
Scale Data

AbstractWe developed a procedure for the cryoEM method MicroED using SerialEM. With this approach, SerialEM coordinates stage rotation, microscope operation, and camera functions for automated continuous-rotation MicroED data collection. More than 300 datasets can be collected overnight in this way, facilitating high-throughput MicroED data collection for large-scale data analyses.

scholarly journals Using Galaxy to Perform Large‐Scale Interactive Data Analyses

2012 ◽  
Vol 38 (1) ◽  
Author(s):  
Jennifer Hillman‐Jackson ◽  
Dave Clements ◽  
Daniel Blankenberg ◽  
James Taylor ◽  
Anton Nekrutenko ◽  
...  
Keyword(s):  
Large Scale ◽  
Data Analyses ◽  
Interactive Data

scholarly journals Advanced Applications of RNA Sequencing and Challenges

2015 ◽  
Vol 9s1 ◽  
pp. BBI.S28991 ◽  
Author(s):  
Yixing Han ◽  
Shouguo Gao ◽  
Kathrin Muegge ◽  
Wei Zhang ◽  
Bing Zhou
Keyword(s):  
High Speed ◽  
Large Scale ◽  
High Sensitivity ◽  
Rna Seq ◽  
Specific Expression ◽  
Sequencing Technologies ◽  
Large Scale Data ◽  
Data Analyses ◽  
Allele Specific ◽  
Differential Gene

Next-generation sequencing technologies have revolutionarily advanced sequence-based research with the advantages of high-throughput, high-sensitivity, and high-speed. RNA-seq is now being used widely for uncovering multiple facets of transcriptome to facilitate the biological applications. However, the large-scale data analyses associated with RNA-seq harbors challenges. In this study, we present a detailed overview of the applications of this technology and the challenges that need to be addressed, including data preprocessing, differential gene expression analysis, alternative splicing analysis, variants detection and allele-specific expression, pathway analysis, co-expression network analysis, and applications combining various experimental procedures beyond the achievements that have been made. Specifically, we discuss essential principles of computational methods that are required to meet the key challenges of the RNA-seq data analyses, development of various bioinformatics tools, challenges associated with the RNA-seq applications, and examples that represent the advances made so far in the characterization of the transcriptome.

scholarly journals QuickRNASeq lifts large-scale RNA-seq data analyses to the next level of automation and interactive visualization

BMC Genomics ◽  
2016 ◽  
Vol 17 (1) ◽  
Author(s):  
Shanrong Zhao ◽  
Li Xi ◽  
Jie Quan ◽  
Hualin Xi ◽  
Ying Zhang ◽  
...  
Keyword(s):  
Large Scale ◽  
Interactive Visualization ◽  
Rna Seq ◽  
Level Of Automation ◽  
Data Analyses

Meta-Analysis

2018 ◽  
Author(s):  
Brian D. Haig
Keyword(s):  
Data Analysis ◽  
Statistical Analysis ◽  
Nature Of Science ◽  
Large Scale ◽  
Scientific Explanation ◽  
Meta Analysis ◽  
Average Effect ◽  
Data Analyses ◽  
Common Domain ◽  
Conceptual Foundations

Chapter 5 is concerned with the conceptual foundations of meta-analysis. It deals with large-scale issues having to do with meta-analysis and the nature of science. Meta-analysis is an approach to data analysis that involves the quantitative, or statistical, analysis of data analyses from a number of existing primary studies in a common domain. At its simplest, meta-analysis involves computing the average effect size for a group of studies. The chapter begins by discussing Gene Glass’s rationale for meta-analysis. It then examines David Sohn’s argument that meta-analysis does not produce genuine scientific discoveries. The roles of meta-analysis in relation to the processes of phenomena detection and scientific explanation are also considered.

The Latest (Dusty) Pieces in the Rosetta Story

2020 ◽  
Author(s):  
Stavro Lambrov Ivanovski
Keyword(s):  
Large Scale ◽  
Collaborative Approach ◽  
Cometary Dust ◽  
Additional Information ◽  
Data Analyses ◽  
Entire Duration ◽  
Comet 67P ◽  
Critical Results

<p>The ESA’s Rosetta spacecraft had the unique opportunity to follow comet 67P/Churyumov-Gerasimenko (hereafter 67P) for about 2.5 years – from January 2014 to September 2016 – observing how the comet evolved while approaching the Sun, passing through perihelion and then moving back into the outer solar system. Remote sensing and in-situ instruments onboard Rosetta acquired data to study the comet’s dust environment during the entire duration of the mission, while telescopes followed the large-scale coma and tails from Earth. Here we report the latest advances of the ongoing multi-instrument approach that the Rosetta dust working group has been following in the recent years. Individual instrument data analyses have been carried on providing a first characterization of 67P dust environment. Timely, multi-instruments data analyses are now progressing a step forward in understanding how comet works and are providing critical results for a more comprehensive and unified knowledge of cometary dust environments. We will illustrate the progress we have made and the results we have reached following this constructive and collaborative approach.</p><p>We also discuss the latest achievements on the cometary dust modelling using the multi-instrument Rosetta data. In particular, what additional information these calibrated dust models provide and what we are still missing in cometary dust characterization.</p>

STEM:  A Software Tool for Large-Scale Proteomic Data Analyses

2005 ◽  
Vol 4 (5) ◽  
pp. 1826-1831 ◽  
Author(s):  
Takashi Shinkawa ◽  
Masato Taoka ◽  
Yoshio Yamauchi ◽  
Tohru Ichimura ◽  
Hiroyuki Kaji ◽  
...  
Keyword(s):  
Large Scale ◽  
Software Tool ◽  
Proteomic Data ◽  
Data Analyses
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